Facial plastic surgery and face recognition algorithms: Interaction and challenges. A scoping review and future directions

A Bibliometric Review of Software Tools in Plastic Surgery: How They Benefit Surgeons and Improve Practice from 2004 to 2024

BACKGROUND

Bibliometric analyses of software applications in plastic surgery are relatively limited. This study aims to address this gap by summarizing current research trends and providing insights that may guide future developments in this field.

METHODS

Data were retrieved from the Web of Science Core Collection. Visualized results, including publication characteristics, disciplines, journals, documents, countries/regions, institutions, authors, and research focuses, were analyzed and optimized using CiteSpace and the "Results Analysis" and "Citation Reports" functions in WoSCC.

RESULTS

A total of 339 publications were identified through manual screening, indicating a general upward trend in annual publications and citations. Key research areas in this field include computer-aided design, modeling, virtual reality, augmented reality, artificial intelligence, social media software, telemedicine, and mobile health. However, challenges such as the high costs of advanced tools, the need for specialized training, and concerns regarding data security and patient privacy are significant barriers. Addressing these issues could be crucial for broader adoption. Despite these obstacles, the potential benefits offered by these technologies suggest their increasing relevance in plastic surgery.

CONCLUSION

Plastic surgery appears to be evolving toward a more standardized, digitalized, and intelligent future. Software has the potential to transform traditional workflows in both research and practice, contributing to improvements in efficiency and precision in the field.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Facial artificial intelligence in ophthalmology and medicine: fundamental and transformative applications

The integration of artificial intelligence (AI) in healthcare, particularly in the domain of facial processing tasks, has witnessed substantial growth in the 21st century. However, this requires sufficient appraisal for clinicians and researchers to adequately understand nomenclature and key concepts commonly used in this field. This article aims to elucidate the diverse applications of facial processing tasks, such as facial landmark extraction, face detection, face tracking, facial expression recognition and action unit detection, and their relevance to ophthalmology and other medical specialties. The keywords 'ophthalmology', 'facial artificial intelligence', 'facial recognition' and 'periorbital measurements' were used on PubMed and Ovid, between September 2012 and September 2022, to identify and screen for eligible articles. Studies reporting on human patients in ophthalmology, plastic, maxillofacial and cosmetic surgery with ocular lesions whose facial biometrics were processed by AI and written in the English language were included. A total of 291 and 513 articles were identified on PubMed and Ovid respectively. Twenty articles were included for analysis in this literature review after duplicates, inaccessible articles and articles without full manuscripts were excluded. Although fully automated algorithms can share the workload in healthcare systems and relieve strains on manpower, rigorous testing is crucial, followed by the challenges of convincing management bodies that it would work in reality, coupled with the costs of implementing specialised functional hardware and software. While patients have a valid concern that it would reduce physical contact with clinicians, it is important for clinicians not to replace clinical decision-making with AI alone.

"Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm

Introduction: Multiple tools have been developed for facial feature measurements and analysis using facial recognition machine learning techniques. However, several challenges remain before these will be useful in the clinical context for reconstructive and aesthetic plastic surgery. Smartphone-based applications utilizing open-access machine learning tools can be rapidly developed, deployed, and tested for use in clinical settings. This research compares a smartphone-based facial recognition algorithm to direct and digital measurement performance for use in facial analysis. Methods: Facekit is a camera application developed for Android that utilizes ML Kit, an open-access computer vision Application Programing Interface developed by Google. Using the facial landmark module, we measured 4 facial proportions in 15 healthy subjects and compared them to direct surface and digital measurements using intraclass correlation (ICC) and Pearson correlation. Results: Measurement of the naso-facial proportion achieved the highest ICC of 0.321, where ICC > 0.75 is considered an excellent agreement between methods. Repeated measures analysis of variance of proportion measurements between ML Kit, direct and digital methods, were significantly different (F[2,14] = 6-26, P<<.05). Facekit measurements of orbital, orbitonasal, naso-oral, and naso-facial ratios had overall low correlation and agreement to both direct and digital measurements (R<<0.5, ICC<<0.75). Conclusion: Facekit is a smartphone camera application for rapid facial feature analysis. Agreement between Facekit's machine learning measurements and direct and digital measurements was low. We conclude that the chosen pretrained facial recognition software is not accurate enough for conducting a clinically useful facial analysis. Custom models trained on accurate and clinically relevant landmarks may provide better performance.

Acquisition of Precision and Reliability of Modalities for Facial Reconstruction and Aesthetic Surgery: A Systematic Review

The foundation of reconstructive and cosmetic surgery is a confluence of advanced technologies, plethora of procedures, inventive modifications, and planned strategies. In surgical planning, the most crucial steps for treating a patient are evaluating the facial morphometry and recognizing the deviations from the baseline values of facial parameters. Various imaging and non-imaging modalities and sub-modalities contribute to diagnosis, treatment planning, and follow-up care. These techniques are an important milestone of pre-, peri-, and postoperative care in facial reconstruction. The current research aims to comprehensively explain imaging and non-imaging technologies encompassing both innovative and traditional approaches in facial reconstruction. PubMed, Scopus, and Web of Science were searched from 1990 to 2022, and systematic review was conducted in accordance with the PRISMA recommendations. Undoubtedly, various factors impact the selection of facial analysis acquisition approaches and their prospective. The surgical team must understand such modalities' potential for diagnosis and treatment. The evolution of three-dimensional imaging has been fueled because of the need for devices with high speed, small size, and several functions. Automation with more efficiency and precision is the way of the future for three-dimensional imaging. Stereophotogrammetry can clearly quantify the field of facial analysis. All the publications under consideration came to the same conclusion: Canfield's Vectra three-dimensional imaging devices can provide accurate, repeatable stereophotogrammetric pictures. Although a few minor mistakes were recorded, most examined devices are deemed reliable and accurate tools for Plastic surgeons.

The Use of Facial Recognition Software and Published Manuscripts to Examine Trends in Surgical Editorial Board Diversity

INTRODUCTION

Recent social justice movements have highlighted the need for improved diversity and inclusion. These movements have emphasized the need for inclusivity of all genders and races in all sectors including surgical editorial boards. There is currently not an established, standardized method to assess the gender, racial, and ethnic makeup of surgical editorial board rosters, yet artificial intelligence is a method that can be utilized to determine gender and race in an unbiased manner. The aim of the present study is to determine if recent social justice movements correlate with an increase in diversity-themed articles published and if there is an increase in the gender and racial makeup of surgical editorial boards determined by artificial intelligence software.

METHODS

Impact factor was used to assess and rank highly regarded general surgery journals. The website of each of these journals was examined for pledges of diversity in their mission statements and core beliefs of conduct. To determine the number of diversity-themed articles that were published during 2016 and 2021, each surgical journal was analyzed for diversity-themed articles using 10 specific keywords in PubMed. To determine the racial and gender makeup of editorial boards in 2016 and 2021, we obtained the current and the 2016 editorial board roster. Roster member images were collected from academic institutional websites. Betaface facial recognition software was used to assess the images. The software assigned the gender, race and ethnicity of the image supplied. Betaface results were analyzed using a Chi Square Test of Independence.

RESULTS

We analyzed 17 surgical journals. Only four of 17 journals were found to have diversity pledges on their website. For diversity themed publications, 1% of articles in 2016 and 2.7% in 2021 were published specifically about diversity. There was a significant increase in the amount of diversity articles/journal published per year in 2016 (6.59) compared to 2021 (25.94, P < 0.001). There was no correlation between impact factor and articles that publish diversity keywords. 1968 editorial board member images were analyzed using Betaface software to determine gender and race in both time periods. There was no significant increase in diversity of editorial board members regarding gender, race, and ethnicity temporally from 2016 to 2021.

CONCLUSIONS

In the present study, we found that although the number of diversity-themed articles has increased over the last 5 y, however the gender and racial makeup of surgical editorial boards has not changed. Further initiatives are needed to better track and diversify the gender and racial composition of surgical editorial boards.

Fingerprinting walking using wrist-worn accelerometers

BACKGROUND

Identifying an individual from accelerometry data collected during walking without reliance on step-cycle detection has not been achieved with high accuracy.

RESEARCH QUESTION

We propose an open-source reproducible method to: (1) create a unique, person-specific "walking fingerprint" from a sample of un-landmarked high-resolution data collected by a wrist-worn accelerometer; and (2) predict who an individual is from their walking fingerprint.

METHODS

Accelerometry data were collected during walking from 32 individuals (23-52 y.o., 19 females) for at least 380 s each. For this study's purpose, data are not landmarked, nor synchronized. Individual walking fingerprints were created by: (1) partitioning the accelerometer time series in adjacent, non-overlapping one-second intervals; (2) transforming all one-second interval data for a given individual into a three-dimensional (3D) image obtained by plotting each one-second interval time series by the lagged time series for a series of lags; (3) partitioning these resulting participant-specific 3D images into a grid of cells; and (4) identifying the combinations of cells (areas in the 3D image) that best predict the individual. For every participant, the first 200 s of data were used as training and the last 180 s as testing. This approach does not use segmentation methods for individual strides, which reduces dependence on complementary algorithms and increases its generalizability.

RESULTS

The method correctly identified 100 % of the participants in the test data and highlighted unique features of walking that characterize the individuals.

SIGNIFICANCE

Predicting the identity of an individual from their walking pattern has immediate implications that can complement or replace those of actual fingerprinting, voice, and image recognition. Furthermore, as walking may change with age or disease burden, individual walking fingerprints may be used as biomarkers of change in health status with potential clinical and epidemiologic implications.

Advancement in Human Face Prediction Using DNA

The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.

Facial Recognition Software Use on Surgically Altered Faces: A Systematic Review

OBJECTIVE

Facial recognition software (FRS) is becoming pervasive in society for commercial use, security systems, and entertainment. Alteration of the facial appearance with surgery poses a challenge to these algorithms, but several methods are being studied to overcome this issue. This study systematically reviews methods used in facial recognition of surgically altered faces.

MATERIALS AND METHODS

A systematic review was performed by searching PubMed and Institute of Electrical and Electronics Engineers (IEEE) databases to identify studies addressing FRS and surgery. On initial review, 178 manuscripts were identified relating to FRS and surgery and allowed division into multiple subgroups. The decision was made to focus on the recognition of surgically altered faces.

RESULTS

Eligible studies included those reports in English on FRS of surgically altered faces, and 39 papers were included. Surgical procedures range from affecting skin surface, such as skin peeling, to altering facial features, such as rhinoplasty, mentoplasty, malar augmentation, brow lift, facelift, orthognathic surgery, facial reanimation, and facial feminization. Methods were classified into appearance-based, feature-based, and texture-based. Descriptive versus experimental protocols were characterized by different reporting outcomes and controls. Accuracy ranged from 19.1% to 85.35% using various analysis methods.

CONCLUSIONS

Knowledge of available limitations and advantages can aid in counseling patients regarding personal technology use, security, and quell fears about surgery to evade authorities. Surgical knowledge can be utilized to improve FRS algorithms for postsurgical recognition.

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Undiagnosed malnutrition is a significant problem in high-income countries, which can reduce the quality of life of many individuals, particularly of older adults. Moreover, it can also inflate the costs of existing health care systems because of the many metabolic complications that it can cause. The current methods for assessing malnutrition can be cumbersome. A trained practitioner must be present to conduct an assessment, or patients must travel to facilities with specialized equipment to obtain their measurements. Therefore, digital health care is a possible way of closing this gap as it is rapidly gaining traction as a scalable means of improving efficiency in the health care system. It allows for the remote monitoring of nutritional status without requiring the physical presence of practitioners or the use of advanced medical equipment. As such, there is an increasing interest in expanding the range of digital applications to facilitate remote monitoring and management of health issues. In this study, we discuss the feasibility of a novel digital remote method for diagnosing malnutrition using facial morphometrics. Many malnutrition screening assessments include subjective assessments of the head and the face. Facial appearance is often used by clinicians as the first point of qualitative indication of health status. Hence, there may be merit in quantifying these subtle but observable changes using facial morphometrics. Modern advancements in artificial intelligence, data science, sensors, and computing technologies allow facial features to be accurately digitized, which could potentially allow these previously intuitive assessments to be quantified. This study aims to stimulate further discussion and discourse on how this emerging technology can be used to provide real-time access to nutritional status. The use of facial morphometrics extends the use of currently available technology and may provide a scalable, easily deployable solution for nutritional status to be monitored in real time. This will enable clinicians and dietitians to keep track of patients remotely and provide the necessary intervention measures as required, as well as providing health care institutions and policy makers with essential information that can be used to inform and enable targeted public health approaches within affected populations.

A comprehensive survey on techniques to handle face identity threats: challenges and opportunities

The human face is considered the prime entity in recognizing a person's identity in our society. Henceforth, the importance of face recognition systems is growing higher for many applications. Facial recognition systems are in huge demand, next to fingerprint-based systems. Face-biometric has a highly dominant role in various applications such as border surveillance, forensic investigations, crime detection, access management systems, information security, and many more. Facial recognition systems deliver highly meticulous results in every of these application domains. However, the face identity threats are evenly growing at the same rate and posing severe concerns on the use of face-biometrics. This paper significantly explores all types of face recognition techniques, their accountable challenges, and threats to face-biometric-based identity recognition. This survey paper proposes a novel taxonomy to represent potential face identity threats. These threats are described, considering their impact on the facial recognition system. State-of-the-art approaches available in the literature are discussed here to mitigate the impact of the identified threats. This paper provides a comparative analysis of countermeasure techniques focusing on their performance on different face datasets for each identified threat. This paper also highlights the characteristics of the benchmark face datasets representing unconstrained scenarios. In addition, we also discuss research gaps and future opportunities to tackle the facial identity threats for the information of researchers and readers.

Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.